Much of the physiology of metazoans is reflected in the temporal and spatial variation of gene expression among constituent cells. Some of this variation is stable and has helped us to define adult cell types, as well as numerous intermediate cell types in development. Other variation results from dynamic physiological events such as the cell cycle, changes in cell microenvironment, development, aging, and infection. Still other expression changes appear to be stochastic in nature, and may have important consequences. To understand gene expression in development and physiology, it has been a dream of biologists to map gene expression changes not only in RNA levels, but also in protein levels, and even to monitor post-translational modifications in every cell.
The methods available today for RNA sequence analysis (RNA-Seq) have the capacity to quantify the abundance of RNA molecules in a population of cells with great sensitivity. With some considerable effort these methods have been harnessed to analyze RNA content in single cells. What is limiting are effective ways isolate and process large numbers of individual cells for in-depth RNA sequencing, and to do so quantitatively. This requires the isolation of cells under uniform conditions, preferably with minimal loss of cells, especially in the case of clinical samples. The requirements for the number of cells, the depth of coverage, and the accuracy of the measurements of RNA abundance will depend on experimental considerations, which will include factors such as the difficulty of obtaining material, the uniqueness of the material, the complexity of the cell population, and the extent to which cells are diversified in gene expression space. Lacking today high capacity single cell transcriptome data, it is hard to know the depth of coverage needed, but the presence of rare cell types in populations of interest, such as occult tumor cells or tissue stem cell sub-populations, combined with other independent drivers of heterogeneity such as cell cycle and stochastic effects, suggests a demand for analyzing large numbers of cells.
Although analysis of RNA abundance by RNA-seq is well-established, the accuracy of single cell RNA-Seq is much more sensitive than bulk assays to the efficiency of its enzymatic steps; furthermore the need for PCR or linear amplification from single cells risks introducing considerable errors. There are also major obstacles to parallel processing of thousands or even tens of thousands of cells, and to handling small samples of cells efficiently so that nearly every cell is measured. Over the past decade, microfluidics has emerged as a promising technology for single-cell studies with the potential to address these challenges. Yet the number of single cells that can be currently processed with microfluidic chips remains low at 70-90 cells per run, which sets a limit for analysis of large numbers of cells in terms of running costs and the limited time during which cells remain viable for analysis. Moreover, capture efficiencies of cells into microfluidic chambers are often low, a potential issue for rare or clinical samples where the number of cells available is limited.